universiti putra malaysia verification and validation...

UNIVERSITI PUTRA MALAYSIA

VERIFICATION AND VALIDATION OF SAMPLING INTENSITIES IN THE PRE-FELLING INVENTORY OF A TROPICAL FOREST

IN PENINSULAR MALAYSIA

WAN MOHD SHUKRI BIN WAN AHMAD

FH 1997 6



By


Thesis Submitted in Fulfilment of the Requirements for the Degree of Master of Science in the Faculty of Forestry,

Universiti Putra Malaysia.

December, 1997

THIS M.SC THESIS IS SPECIALLY DEDICATED TO ............. .

NORAINI BINTI MOHD ZAHID .......... mama

WAN MUHAMMAD AQIF SHAHlRAN. .......... aqif

WAN NUR ARINA SYUHADA .......... erin

ACKNOWLEDGEMENTS

In the name of Allah, the most Benevolent and most Merciful.

The author is indebted to a number of people and organisations for making

possible completion of this study.

The encouragement given by the Director General and Deputy Director General

of Forest Research Institute Malaysia (FRIM), Kepong, and the support from Faculty

of Forestry, Universiti Putra Malaysia (UPM), are gratefully acknowledged.

My SIncere appreciation goes to Assoc. Prof. Dr. Kamis Awang, for his

dedicated effort, active participation and constant encouragement throughout this

study.

Profound appreciation and thanks are extended to my thesis co-supervisor

Assoc. Prof. Ashari Muktar and Dr. Wan Razali Wan Mohd of FRIM for his

guidance, assistance, stimulating discussion, and interest during the planning and

execution of this study.

Many thanks are further due to Mr. Chong Phang Fee of FRIM for assisting me

in completing data management on the subject on this study.

The work of all lecturers and staff of these two organisations in helping me,

directly or indirectly are gratefully acknowledged.

Lastly but not least, me deepest gratitude to my family for being very

understanding and for their constant prayers for my success.

III

Standard acknowledgements in the use of the Pasoh SO-ha plot data

The large-scale forest plot at Pasoh Forest Reserve is an ongoing project of the

Malaysian Government, initiated by the Forest Research Institute Malaysia through its

former Director-General, Salleh Mohd. Nor, and under the leadership of N.

Manokaran, Peter S. Ashton and Stephen P. Hubbell. Supplemental funds are very

gratefully acknowledged from the following sources: National Science Foundation

(USA) BSR Grant No. INT-84- 12201 to Harvard University through P. S. Ashton and

S. P. Hubbell; Conservation, Food and Health Foundation, Inc. (USA); United

Nations, through the Man and the Biosphere program, UNESCO-MAB grants

2 1 7.65 1 .5 , 2 1 7.652.5, 243 .027.6, 2 1 3 . 1 64.4, and also UNESCO-ROSTSEA grant No.

243 . 1 70.6.; and the continuing support of the Smithsonian Tropical Research Institute

(USA), Barro Colorado Island, Panama.

IV

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS .................................. ....................................... 111 LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii LIST OF FIGURES ..................................................................................... IX LIST OF ABBREVIATIONS ..................................................................... X ABSTRACT ................................................................................. ............... Xl ABSTRAK ................................................................................................... X111

CHAPTER

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forestry in Malaysia . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statement of Problems / Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 LITERATURE REVIEW . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . ... . . . . . . . . . . Forest Inventory / Sampling Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest Sampling Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling Technique in Selected Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest Inventories in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 MElliODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Study Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Site Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plot Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Model Development / Calibration Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model Validation Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 RESULTS AND DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling Intensity . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . .

Sampling Intensity: Diameter Classes . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling Intensity: Species Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . Sampling Intensity: Plot Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Comparison of Calibration and Validation Data Sets . . . . . . . . . . . . . . . . . . . . . .

5 CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

REFERENCES

APPENDIX

1 Species list for 30 ha Model Development / Calibration

data area (trees �5cm dbh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v

1 3

6 7

8 8

10 14 17

20 20 22 22 24 27 30

33 33 35 35 38 45 48

54

56

63

2 3 4 5

INV.PRG dBase 5.0 program . . . . . . . . ...... . . . . . . . . .. . . . . ........ . . .. . . . . . . . . PASTEST.PRG dBase 5.0 program ..................................... . SUMALL.PRG dBase 5.0 program ...................................... . SUMRRY.PRG dBase 5.0 program ..................................... .

78 81 82 84

VITA . .. ...... ................................. ..................................... 86

VI

LIST OF TABLES Page

Table

1 Inventory Plot Information ......... .......................................... 5

2 Total Tree Number, Basal Area and Volume by Species Group for the 30 ha Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3 Total Tree Number, Basal Area and Volume by Tree Size Class for the 30 ha Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4 Number of Species in 30 ha Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5 The Percent Sample Required by Tree Size Class for All Species Groups at Various Confidence and Error Levels . . . . . . . . . . . . . . . . . . . . 36

6a The Percent Sample Required by Species Groups for All Tree Size Classes (�5 cm dbh) at Various Confidence and Error Levels in 1 0 m x 1 0 m Plot Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6b The Percent Sample Required by Species Groups for All Tree Size Classes (�5 cm dbh) at Various Confidence and Error Levels in 20 m x 25 m Plot Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6c The Percent Sample Required by Species Groups for All Tree

Size Classes (�5 cm dbh) at Various Confidence and Error Levels in 20 m x 50 m Plot Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

7a The Percent Sample Required by Species Groups in 1 0 m x 1 0 m Sample Plot (Tree Size Class 5 - <1 5 cm dbh) . . . . . . . . . . . . . . . . . . . . . . . . 42

7b The Percent Sample Required by Species Groups in 20 m x 25 m Sample Plot (Tree Size Class 1 5 - <30 cm dbh) . . . . . . . . . . . . . . . . . . . . . . 43

7c The Percent Sample Required by Species Groups in 20 m x 50 m Sample Plot (Tree Size Class 30 - <45 cm dbh) . . . . . . . . . . . . . . . . . . . . . . 43

7d The Percent Sample Required by Species Groups in 20 m x 50 m Sample Plot (Tree Size Class �30 cm dbh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

7e The Percent Sample Required by Species Groups in 20 m x 50 m

Sample Plot (Tree Size Class �45 cm dbh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

8 The Percent Sample Required by Tree Size Classes at Various Confidence and Error Levels in 1 0 m x 1 0 m, 20 m x 25 m and 20 m x 50 m Plot Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

VB

9 The Value Differences Between Development and Validation Plots of the Study Area .......................................................... . 49

10 Comparison of Model Development and Model Validation of the Study Area . . . . . .. . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . .. . . . . . . . . .. . . . . . . . 50

11 Interval Estimate on Sampled Measurements of Model Validation Using 95% Confidence Limit ............................. . . 52

Vlll

Figure

1

2

3

4

5

LIST OF FIGURES

Layout of Sample Units (plots) in Systematic Sampling ...... .

Various Plot Sizes for the Pre-felling Inventory ................... .

Map of Peninsular Malaysia Showing the Location of Pasoh Forest Reserve ........................................................... .

The 50ha Plot is Subdivided into Column (20 m x 500 m), Row (20 m x 1 000 m), Quadrats ( 2 0 m x 2 0 m) and Subquadrats ( 5 m x 5 m) ...................................................... .

Topographic Map of the PFR 50 ha Plot at 1 m Contour Interval and Gridded at 1 ha Units ....................................... .

IX

Page

18

21

23

2 5

26

FRIM

FDPM

PFE

MUS

SMS

Pre-F

dbh

DM

DNM

ALL DIPT

ND.LHW

ND.MHW

ND.HHW

MIse

ALL NON-DIPT

PFR

LIST OF ABBREVIATIONS

Forest Research Institute Malaysia

Forestry Department of Peninsular Malaysia

Permanent Forest Estates

Malayan Uniform System

Selective Management System

Pre-felling

Diameter at breast height

Dipterocarp, Meranti

Dipterocarp, Non-Meranti

All Dipterocarps

Non-Dipterocarp, Light Hardwoods

Non-Dipterocarp, Medium Hardwoods

Non-Dipterocarp, Heavy Hardwoods

Miscellaneous

All Non-Dipterocarps

Pasoh Forest Reserve

x

Abstract of thesis presented to the Senate ofUniversiti Putra Malaysia in fulfilment of the requirements for the degree of Master of Science.



By


December, 1997

Chairman: Associate Professor Kamis Awang, Ph. D.

Faculty : Forestry

This study was carried out on a randomly chosen 40 ha (8 00 m x 500 m) forest

area within the 50 ha Demography Project of the Forest Research Institute Malaysia

(FRIM) in Pasoh Forest Reserve, Negri Sembilan. A total of 3 0 ha out of 40 ha study

area were used as a Calibration data set and the 1 0 ha remaining area as a Validation

data set. The percent sample or intensity of sampling was determined at various

confidence and error levels, viz., from 8 0 % to 9 5 % confidence with ± 5 % to ±2 0 %

error in estimating tree density, basal area and volume. Results obtained from both

calibration and validation data sets were used to assess the notion that the statistical

reliability of the SMS' s Pre-felling inventory (by Forestry Department Peninsular

Malaysia) is at 9 5% confidence and ±2 0 % error levels.

Results showed different intensities of sampling regarding classification into

diameter, plot size and species groups to the three parameters measured at different

confidence and error levels. At 1 % sampling intensity to inventorise density of trees

5 cm - <1 5 cm dbh in 1 0 m x 1 0 m plot, the confidence and error levels of

respectively 9 5 % and ± 1 5 % or 9 0 % and ± 1 0 % would be better reliability levels. The

9 5 % and ± 1 5 % levels were also true for estimating basal area and volume. At 5 %

sampling intensity of trees 15 cm - <3 0 cm dbh (20 m x 2 5 m plot), its reliability

Xl

was at 9 0 % confidence and ± 1 0 % error limits for either tree density, basal area or

volume estimation. However, at 1 0 % sampling intensity of trees �30 cm dbh

( 2 0 m x 50 m plot), the 9 5 % confidence and ±2 0 % error held true in estimating tree

volume. Classification into species groups showed that higher sampling intensity was

needed to sample volume of Dipterocarps species compared to tree density, basal area

and other species groups at any confidence and error levels. Results also showed that

plot 10m x 10m yielded lower sampling intensity for trees <30cm dbh and plot

2 0 m x 2 5 m for trees �30 cm dbh at any confidence and error levels. The

implications of attaching confidence and error levels to the sampling intensity are also

discussed.

XlI

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Master Sains

MENGUJI DAN MENGESAHKAN INTENSITI PERSAMPELAN INVENTORI SEBELUM TEBANGAN HUTAN TROPIKA

DI SEMENANJUNG MALAYSIA

Oleh


Disember, 1997

Pengerusi: Profesor Madya Kamis Awang, Ph. D. Fakulti : Perhutanan

Kajian ini telah dijalankan di kawasan seluas 40 ha (8 00 m x 500 m) yang

dipilih secara rawak yang terletak di dalam kawasan Projek Demografi 50 ha oleh

Institut Penyelidikan Perhutanan Malaysia (FRIM) di Rutan Simpanan Pasoh, Negeri

Sembilan. Seluas 3 0 ha dari 40 ha dari kawasan tersebut digunakan sebagai kawasan

set Data Kalibrasi dan 10 ha sebagai kawasan set Data Kesahihan. Intensiti

persampelan ditentukan pada pelbagai tahap keyakinan dan tahap ralat iaitu dari

8 0 % hingga 9 5 % keyakinan dengan ±5 % hingga ±2 0 % ralat dalam menganggarkan

persempalan bilangan pokok, luas pangkal dan isipadu. Keputusan yang diperolehi

dari ke dua-dua set Data Kalibrasi dan set Data Kesahihan digunakan bagi

menentukan bahawa inventory sebelum tebangan yang diamalkan oleh Jabatan

Perhutanan Semenanjung Malaysia ialah pada tahap keyakinan 9 5 % dan ralat ±2 0 %.

Keputusan menunjukkan intensiti persampelan yang berbeza berdasarkan

kepada pengkelasan diameter, kumpulan spesis dan saiz plot terhadap ketiga-tiga

parameter yang diukur pada tahap keyakinan dan ralat yang berbeza. Tahap

keyakinan 9 5 % dan ralat 15 % atau 9 0 % dan 10 % didapati lebih menghampiri pada

intensiti persampelan 1 % yang digunakan bagi penyampelan bilangan pokok

5 sm - <15 sm diameter paras dada (dpd) dalam plot 10 m x 10 m. Tahap keyakinan

Xl11

95% dan ralat 15% juga didapati boleh digunakan bagi penyampelan luas pangkal dan

isipadu. Penyampelan pokok bersaiz 15 sm - < 3 0 sm dpd. dalam plot 20 m x 25 m

bagi menganggarkan bilangan, luas pangkal dan isipadu adalah lebih teliti sekiranya

dilakukan pada tahap keyakinan 9 0% dan ralat ± 10%. Walau bagaimanapun,

intensiti persampelan 10% adalah benar bagi menyampel pokok � 3 0 sm dpd.

pada plot 20 m x 50 m. Pengkelasan kepada kumpulan spesis menunjukkan bahawa

penyempelan isipadu spesis Dipterokap memerIukan intensiti yang lebih tinggi

berbanding bilangan pokok, luas pangkal dan lain-lain kumpulan spesis pada semua

tahap keyakinan dan ralat, saiz plot serta kelas diameter. Keputusan juga

menunjukkan saiz plot 10m x 10m adalah memerIukan intensiti penyempelan yang

lebih rendah bagi pokok < 3 0 sm dpd dan saiz plot 20 m x 25 m bagi pokok � 3 0 sm

dpd pada semua tahap keyakinan dan ralat yang digunakan. Implikasi hubungan

tahap keyakinan dan ralat dengan intensiti persampelan turut dibincangkan.

XIV

CHAPTERl

INTRODUCTION

The tropical rainforest is probably one of the most complex ecosystems in the

world. It is a dynamic system possessing a very high order of organisations in which

physiological, morphological and ecological features in individual plant and animal

members are strongly linked to form a variable continuum in time. The fauna and

flora are numerous and diverse. The richness of its flora and fauna makes it an

important factor, for example in the conversion of carbon dioxide to oxygen. The

information on the fauna and flora is still much to be discovered, especially in terms

of their values to man and their contributions to the working of the ecosystem.

The flora of tropical rainforest is estimated to compnse 25,000 speCIes of

flowering plants (van Steenis, 1971, quoted by Whitmore, 1975) which is about 10%

of the world flora. In Peninsular Malaysia alone there are over 1,600 species of trees

which grow to 90 cm girth and larger (Whitmore, 1975) but of which only about 650

species are currently considered to be of economic value (Peh and Wong, 1980).

Various attempts have been made to summarize the composition of the world's

tropical forest; some are locally based and others are very complex in description.

One very useful description of tropical forest type given by Slee (1983) based

on Schimper (1903). The forest is subdivided into three major categories (i) wet

2

evergreen, (ii) moist deciduous and (iii) dry deciduous. He had also given some

special categories, such as mangrove, bamboo and coniferous.

Forest inventory and monitoring IS a key issue when managing valuable

renewable forest resources. No sensible forest policy is possible if the quantity and

quality and the changes and trends of forests are not known.

Forest inventories have been conducted since the end of the middle ages when

concerns about the wood supply for firewood grew more seriously. In earlier times,

all stems in the forest were counted and some cases even numbered to provide a

complete inventory for planning production. Later, this way changed to a system

where "representative" sections of the forest were selected and these sections

measured completely. The total was then calculated by a blow up factor. This

method has been used by the early foresters, often with good success, provided the

selected area was truly representative of the forest condition (Dieter, 199 3).

Silviculture research in tropical rainforests over the last twenty years has

attempted to develop and synthesize knowledge on the establishment and

management of plantation for timber production. It has also focused on the dynamics

of natural forests, with and without human interventions.

Tropical rainforests have provided commercial timber and fuelwood over two

centuries, and have been managed under several silvicultural systems for the last five

decades. These systems tried to increase the amount and quality of timber produced

by manipulating stand density and composition. These systems often relied on rules

concerning pre-felling (Pre-F) and post-felling (Post-F) inventories, and timber stand

improvement. The choice of the management regime may depend on the types of

forest, the timber market and the forest policy of a country or forest owner.

3

Forestry in Malaysia

Malaysia's natural forests continue to play a major role in the economy,

contributing much to the development of forestry-based industries, of primary as well

as downstream manufacturing activities.

The natural forest resources of Peninsular Malaysia were maintained at a level of

about 6.0 million ha or 45% of the total land area in 1995 (Kementerian Perusahaan

Utama, 1996). They included 4.7 million ha of Permanent Forest Estates (PFE), 0.7

million ha of Stateland Forest Estates and about 0.6 million ha of Wildlife Reserve.

The Permanent Forest Estates constituted about 78% of the total forest resources,

while Stateland Forests constituted 12%. Wildlife reserves made up 10% of the total

forest areas. The main types found are Inland forests (5.54 million ha or 93%), Peat

Swamp forests (0.29 million ha or 5%) and the Mangroves forests (0.11 million ha or

2%). The Peat Swamp forests are mainly located in Selangor, Pahang, Terengganu

and lohor while the Mangrove forests are found mainly in the coastal areas of Perak,

lohor, Selangor and Kedah.

Malaysia has a land mass of 32.90 million ha., of which almost 58.1% (19.12

million ha) is covered with natural forests. Tree cover is further complemented by

another 14.6% of plantations, mainly of oil palm, rubber and cocoa. This figure

makes Malaysia one of the most forested countries in the world with 72.7% of its land

mass covered with trees.

Malaysia'S forestry policy requires adequate establishment of Permanent Forest

Estate (PFE) in order to ensure a continuous supply of forest resources. The PFE

covers about 42.6% (14.01 million ha.) of Malaysia's total land area. In addition, 2.12

million ha. outside of the PFE are also designated as national parks and wildlife

4

sanctuaries. Thus the total area of natural forest under management amounts to about

16 million ha (Kementerian Perusahaan Utama, 1996).

The forestry sector in Malaysia has always played an important role in the

national economy. In 199 5 it contributed about RM11 billion in export earnings to

the country. This constituted 7% of the total export earnings. It also provided direct

employment for about 243, 007 persons (Kementerian Perusahaan Utama, 1996). To

manage this valuable forest, various silviculture systems have been developed in

Malaysia. The historical development of natural forest silviculture led to the Malayan

Uniform Systems (MUS) (Wyatt-Smith, 1963). MUS has been the basic silvicultural

system used since the 19 50's. It was developed for lowland dipterocarp forests rich in

Meranti (Shorea spp.) and Kerning (Dipterocarpus spp.). However, MUS has not

been effective in the hill forest due to the comparatively more difficult terrain, uneven

stocking, lack of natural regeneration on the forest floor before logging because of

irregular seeding from potential mother trees (Thang, 1986). Since the bulk of the

permanent forest estate now consists of hill forests, the system has now been changed

to the Selective Management Systems (SMS), a polycyclic logging system in which a

2 5-3 0 year cutting cycle is anticipated.

The MUS consists of removing the mature crop in one single felling of all trees

down to 45cm dbh for all species and releasing the selected natural regenerations of

varying ages, which are mainly the light-demanding medium and light hardwood

species. This system has been successfully applied to the lowland dipterocarp forest,

but has been found to be unsuccessful in the hill dipterocarp forest (Wyatt-Smith,

1963).

Consequently, the Selective Management System (SMS) was introduced in 1978

to allow for more flexible timber harvesting regimes which are consistent with the

need to safeguard the environment and at the same time to take advantage of the

5

demand of the timber market. More importantly, it discourages the poison-girdling of

the presently uncommercial species which will not only conserve the wood but also

the genetic resources available in the forests for the future.

The SMS requires the selection of management (harvesting) regimes based on

inventory data which will be equitable to both loggers and forest owner as well as

ensuring ecological balance and environmental quality. In practice, under the SMS

the next cut is expected in 30 years after the first logging with an expected net

economic outturn of 30-40 m3 per ha enriched with dipterocarp species.

Current Pre-F forest inventory practice uses different plot sizes resulting in

different inventory intensities. For example, the main inventory plot of 20 m x 50 m

has an inventory intensity of 10% and 20 m x 25 m uses inventory intensity of 5%.

Table 1 explains briefly on the inventory plots of the current Pre-F inventory.

Table 1: Inventory Plot Information

Size Area Inventory Tree size (m) intensity class

m2 Ha (%) Plot

First 50 x20 1000 0.1000 10.00 �4S em diameter 30 em to <45 em diameter

Seeond 25x20 500 0.0500 5.00 15 em to <30 em diameter

Third 10 x 10 100 0.0100 1.00 Sem to <15 em diameter

Fourth 5 x 5 25 0.0025 0.25 1.5 m height to <5 em diameter

2 x 2 4 0.0004 0.04 15 em height to <1.5 m height

Fifth

These different intensities of inventorying the forest have been applied since

SMS was introduced. Little research has been done to verify the accuracy of the

sampling intensities used and whether such intensity was used to inventory the

volume, basal area or density of trees in a given stand.

6

Concerns about the accuracy of current practices of the Pre-F inventory

intensities led to the initiation of this project to be proposed.

Statement of Problems/Justification

The need for quantitative tree data has made it necessary to gIVe senous

consideration to methods of forest sampling. It is important to recognise the need

for adequate sampling and what constitutes an adequate sample.

In every country, there are different conditions for forestry practice and different

requirements for the information system. Therefore, an optimum inventory

design/technique will always be a unique one.

The problem of quality and quantity assessment of the growing stock, especially

in the course of tropical forest inventories, has been tackled by several authors during

the past years (Wyatt-Smith, 1966; Wong, 1976; Ashton, 1976). However, these

research projects cannot yet be considered as final and the approaches proposed are

not yet sufficiently tested in practice. Therefore, a survey of the present situation of

this important sector of inventory needs to be done.

Forest planning for exploitation of tropical forests for sustained yield

management is not feasible without accurate inventory result. Since methods of data

collection have made extraordinary progress during the last two decades, an efficient

synopsis of the techniques of forest inventory is urgently needed.

Zohrer and Forster ( 1987) stated that in forest inventory, highly qualified experts

are necessary to develop an optimum inventory methodology and adequate techniques

for the given purpose. Otherwise, the obtained information is unreliable, misleading

and therefore, useless. A successful forest resource inventory yields the advantages of

7

the strategic planning of forestry can be based on sound information. Hopefully,

with this kind of research, many skill persons on forest inventory can be trained.

In Peninsular Malaysia forest sampling to determine the stocking available

before commercial felling (Pre-F inventory) usually varies between 1 to 10% intensity

for the three plot sizes, viz, 10 m x 10 m, 20 m x 25 m, 20 m x 50 m. The confidence

and error level used are 95% and 20% respectively (Wan Razali, 1995). However,

little research has been done to verify the accuracy of these current Pre-F inventory

practices.

Verification of the current sampling intensities is important to reorganise the

forest inventory practices aiming at introducing sustainable management of the forest

resources. It is the basis for the work of field officers, managers and research officers

alike. Without accurate measurements, efficient designs for inventories, data

processing based on sound statistical procedures and without the knowledge of the

trees' growth, sustainable management with high outtum can never be put into

practice.

Objectives

The objectives of this study are:

1) to verify or validate the accuracy of the Pre-F sampling intensities as currently

practised by the Forestry Department of Peninsular Malaysia and others that use

the same method;

2) to determine whether such inventory intensity was used to inventory the volume,

basal area or density of trees in a given stand; and

3) to determine the accuracy of statistical reliability, i.e. confidence and error levels

associated with the Pre-F inventory of the Forestry Department of Peninsular

Malaysia.

CHAPTER 2

LITERATURE REVIEW

Forest Inventory/Sampling Definition

Forest inventory is the enumeration of pertinent data such as volume, increment,

mortality, etc., for the assessment of productivity of a forest at partiCUlar point in time

(Jeffers, 1985). Repeated continuous inventory implies the regular enumeration of the

productivity in such a way as to provide a continuous check on the changes taking

place in that characteristic of the forest.

Husch et al. (1982) defined forest inventory as the procedure for obtaining

information on the quantity and quality of the forest resource and many of the

characteristics of the land area on which the trees are growing. Most forest

inventories have been, and will continue to be, timber estimates. An inventory is an

information-gathering process that provides one of the bases for rational decisions.

The decisions may be required for the purchase or sale of timber or for preparation of

timber-harvesting plan in forest management.

Forest Mensuration and Inventory technical working group of ASEAN Institute

of Forest Management in its report defined forest inventory as a survey of forest area

to determine such data as area condition, timber, size, volume, and species, rates

of change for specific purposes such as planning, purchase, evaluation

8

9

management, or harvesting. While Pre-F inventory was defined as an inventory

carried out to assess royalty payments to be collected and to determine the economic

viability of a possible extraction (AIFM, 1987).

Sukhatme (1970) stated that a sampling method is a scientific and rational

procedure of selecting units from the population and providing a sample that is

expected to be representative of the whole population. In other word, by utilizing the

sampling method it is possible to estimate the population total, average or proportion

while at the same time reducing the size of the survey operation. Much time and

labour have been saved by the application of the theory of sampling to tackle

problems which require collecting and recording a large number of observations.

The concept of inventory may, of course, be extended to the characteristics, for

example the health of the forest, or residues of pesticides, herbicides and pollutants.

It may also be extended to assessments of damage due to various pests, for example

insect pests and deer, recreation, windblow, or erosion. However, to be effective, the

result of the inventory needs to be related to the appropriate forcing function or

functions, and such relationship can only be inferred from an understanding of the

dynamics of change. It is also important to be able to measure the state of the forest

at any one point in time. Such measurement requires particular types of sampling

design (Ware and Cunia, 1962).

In Malaya, the concept of sample plot was first established in 1901 by the late

Mr. A.M. Burn-Murdock, the first Conservator of Forest (Setten, 1954). In 1915,

the whole subject of sample plot was recognised at the First Malayan Forest

Conference presided over by Mr. G.E.S. Cubitts, the second Conservator of Forest.

10

Forest Sampling Studies

Forestry, in its most basic form, involves the utilisation of wood and therefore

of trees from forests in a most sustainable way. Ecologically, harvesting or logging is

a retrogressive step of succession and the degree of reversion is affected by the

intensity of logging. It is obvious, therefore, that by controlling logging through

proper management and planning, the effects of logging can be reduced and any

set-back minimised so that natural succession can occur to reforest the area again.

Forest management planning is dependent upon the dynamics of the forest. It

involves the integration of all forestry disciplines and requires an understanding of all

characteristics of the forest resource. The most important characteristics of the forest

resource is that it is renewable and that it changes overtime, making sampling in

inventory of forest resources vital to the forestry activities.

People have been making estimates on the extent of forest resources since they

first started staying at one place and staking out territorial claims to the land.

Information sought was if the lands could supply the needed game, fuel, and building

material (Lund, 1993).

In 1946, the first electronic digital computer was developed. By 1950,

automated data processing (ADP) gained popularity in the field of inventory. Users

found the computer enables them to process information faster and with less error. It

also permitted foresters to carry out previously complicated and cumbersome

calculations. This capability led to the development and use of more

statistically-complex, but efficient sample designs (Lund, 1993).

The application of sampling in forestry problem is not new. Many studies have

been successfully conducted based on sampling theory and technique such as

universiti putra malaysia verification and validation...

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